AI Architect in Hampshire

Reports to: Chief AI Officer (line management)

Location in structure: AI/Data architecture area — embedded within the delivery area it serves, line-managing centrally to the Chief AI Officer

Experience: Senior — 6+ years in software, data, or platform engineering/architecture, with meaningful hands-on time on production AI systems, not just pilots

Direct reports: None. Influence comes from judgement and presence in the right conversations, not headcount.

Why this role exists: AI decisions at Citation now cut across Product, Engineering, Security, Infrastructure, and the Business simultaneously — model selection, data architecture, cost, and risk are no longer separable concerns. Without a dedicated architectural owner, each initiative makes these calls independently: patterns diverge, risk goes unspotted until it's expensive, and nobody owns the AI-specific decisions that don't belong wholly to any one function. This role formalises that into a mandate with the standing and scope it needs.

How this role sits in the architecture function: The AI Architect is Citation's dedicated architect for the AI/Data area, sitting alongside the architects covering Human Resources, Business Systems, Health. This role owns the AI-specific application of it, escalating decisions with consequences beyond AI/Data to the Architecture Review Board rather than deciding them alone.

What good looks like: The clearest sign this role is working: AI initiatives at Citation start well and stay on track architecturally. In practice that means:

• Established patterns are the default starting point for new builds, not something teams discover after the fact
• Design questions are resolved before Engineering starts building, not during or after
• Third-party Statements of Work are assessed architecturally before they're signed
• Model and hosting choices are made against a documented decision framework, not habit or vendor pressure
• Token spend and cost-per-outcome are tracked and explainable, not a surprise on the invoice
• Security is involved in every significant initiative from the start, not introduced at the end
• Leadership has a current, accurate view of AI architectural risk and direction, with no significant surprises

Responsibilities:

• Architectural standards and patterns: Own the design patterns Citation builds its AI systems to: retrieval and grounding approaches for systems that need Citation's own knowledge rather than a model's general training, agent orchestration patterns and tool-calling conventions for multi-step and multi-agent work, prompt construction and guardrail design, and the routing logic that decides which model handles which step. Keep these current as the landscape moves, and write decisions down in a form Engineering and Product can actually build against — architecture decision records, not a slide deck. Align standards to Citation's five-layer AI platform architecture.
• Model selection: Hold a documented, evidence-based framework for choosing between closed frontier models accessed through a provider's API (Anthropic, OpenAI, Google) and open-weight models run on Citation's own infrastructure (Llama, Mistral, Qwen and similar) — and apply it per workload, not as a single blanket choice.
• Tokenomics: Understand and actively manage the unit economics of every AI solution recommended, including modelling likely token cost at design time, before a system is built, so the business knows what it's signing up for.
• Data architecture and readiness: Assess whether the data behind any AI initiative is structured, accessible, and reliable enough to support the intended behaviour — across Citation's Salesforce, Atlas, Snowflake, and integration layers — and flag readiness issues before they become delivery blockers.
• Design authority and governance: Hold the approval path for new AI architectural patterns and material departures from existing standards.
• Proof of concept and technical validation: Define scope and success criteria for AI spikes and proofs of concept before they start, validate vendor and partner capability claims before they're embedded in a committed design.
• Third-party delivery oversight: Act as the internal architectural counterpart to Citation's delivery partners, reviewing proposals and Statements of Work before commitments are made.
• Production quality, observability, and cost: Set the standard for how AI systems are monitored and evaluated once live — offline and online evaluation, drift detection, guardrails — and own the response framework for AI incidents in production.
• Security and compliance: Bring Security in as a standing stakeholder on every significant AI initiative, addressing data boundaries, personal data handling, prompt injection risk, and third-party model provider assessments early rather than at the end.
• Responsible AI: Define and maintain guardrails on what AI systems should and shouldn't decide autonomously, ensure outputs are explainable where they affect clients or employees.
• Internal enablement: Run informal sessions or working groups to build AI literacy across Engineering, Product, and the Business.
• Horizon scanning and strategic input: Monitor the AI landscape and evaluate new tools, models, and platforms against Citation's actual needs before they gain internal momentum on hype alone.

What this role requires:

• Must have 6+ years in software, data, or platform engineering or architecture roles, including meaningful hands-on time on production AI systems.
• Practical experience choosing between closed frontier models and open-weight/self-hosted models for real workloads.
• Working knowledge of AI unit economics — token pricing, routing, caching, context engineering.
• Grounding in Citation's actual stack: Amazon Web Services' AI services, agentic and multi-model patterns.
• Comfortable working with regulated, personal-data-heavy employment and compliance data.
• Working knowledge of information security standards and data protection law as they apply specifically to AI systems.